Airborne/spacebased radar STAP using a structured covariance matrix

The mismatch between the clutter and noise power of the prior knowledge and true interference covariance matrix degrades the performance of fast maximum likelihood with assumed clutter covariance (FMLACC) algorithm significantly. By introducing a scale parameter to flexibly adjust the prior power, the authors propose an algorithm which is more robust to the power mismatch than FMLACC algorithm. They also develop a more straightforward method to derive the maximum likelihood covariance matrix estimator under this scaled knowledge constraint. Moreover, they study the problem of automatically determining the scale parameter. The authors provide two parameter selection methods, the first of which is based on estimating the minimum eigenvalue of the prewhitened sample covariance matrix, and the second is based on cross validation. To reduce the computational complexity, they also develop fast implementations for the parameter selection based on cross validation. Numerical simulations demonstrate the performance enhancement of the proposed algorithm compared with FMLACC algorithm in cases of mismatched prior knowledge.

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“…Here we propose a more straightforward method than that in [21] and [22] to solve (19). Let (20) and note that…”

Section: Robust Fmlacc Algorithmmentioning

confidence: 99%

Robust fast maximum likelihood with assumed clutter covariance algorithm for adaptive clutter suppression

Tang

Zhang

Tang

2014

IET Radar, Sonar & Navigation

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“…R ww is estimated from available sensor data, or from receiver output signals with the receiver input blocked, or other means. R αα is estimated adaptively using previously-recorded data [20], a priori information [21], structured covariance techniques [22], analytical modeling techniques [23,24], or combinations of these methods. In fact, the methodology proposed here benefits directly from the large number of covariance estimation techniques developed in the adaptive array and space-time adaptive processing (STAP) areas.…”

Section: Waveform Selection Using Mutual Informationmentioning

confidence: 99%

Waveform diversity via mutual information

Roman

Davis

Garnham

et al. 2009

Digital Signal Processing

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“…Clutter suppression algorithms under nonstationary scenario, including non-side looking arrays (Wang et al 1997), conformal arrays (Zatman 2000;Xie and Wang 2009) and distributed arrays (Melvin et al 2003;Lapierre et al 2003), have also been studied over these years. In addition, several low-sample support algorithms have been proposed for sample shortness situations (Parker and Swindlehurst 2003;Roman et al 2000;Gerlach and Picciolo 2003;Melvin et al 1998;Befgin et al 2006;Zhao et al 2014). The aforementioned methods can be used only for a certain nonhomogenerous scenario.…”

Section: Introductionmentioning

confidence: 99%

A deterministic auto-regressive STAP approach for nonhomogenerous clutter suppression

Duan¹,

Xie²,

Wang³

et al. 2014

Multidim Syst Sign Process

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Direct data domain (DDD) space-time adaptive processing methods avoid nonhomogenerous training samples and can effectively suppress the clutter within the test range cell. However, it suffers inevitable performance loss due to the spatial and temporal smoothing process. Furthermore, the clutter suppression ability of these methods sharply degrades when applied to non-uniform and non-linear array for airborne radar. In this paper, a novel clutter suppression approach in the direct data domain is proposed, which describes clutter characteristic of the test range cell with AR model. For convenience, the novel method is referred to as D 3 AR. It utilizes the most system DOF. Hence, it suffers less aperture loss, compared to conventional DDD methods, e.g., the direct data domain least squares (D 3 LS). More importantly, D 3 AR can achieve much better clutter suupression prformance than D 3 LS when applied to conformal array airborne radar because it does not need the spatial smoothing. The effectiveness of the D 3 AR is verified by numerical examples for the case of a circular array.

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Airborne/spacebased radar STAP using a structured covariance matrix

Cited by 56 publications

References 19 publications

Robust fast maximum likelihood with assumed clutter covariance algorithm for adaptive clutter suppression

Robust fast maximum likelihood with assumed clutter covariance algorithm for adaptive clutter suppression

Waveform diversity via mutual information

A deterministic auto-regressive STAP approach for nonhomogenerous clutter suppression

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